The present invention relates generally to estimations of health for batteries used in heavy power use applications, and more particularly to a state of health for electric vehicle and hybrid vehicle batteries and battery packs.
It is important for users and manufacturers of vehicles using batteries that the health and performance of those batteries be monitored. Batteries lose power and capacity (the specific mechanisms of that loss vary based upon cell chemistry) therefore it is important that the power and capacity be known to aid in service diagnostics and power limit algorithms during usage. The following description is specifically focused on lithium-ion chemistry but other chemistries may be benefited from the following description.
Current techniques for monitoring the battery health include battery capacity measurements and estimates. This is an important battery parameter, but it is the case that a battery having sufficient capacity may cause a user, under certain conditions, to experience a sudden loss in available power from the battery.
One way to estimate available power is based upon measurement of AC impedances of the batteries, using conventional techniques such as Kalman filtering. Knowledge of AC impedance allows accurate real-time DC power estimation. However, since the real-time impedance is a function of state of charge and health, this estimation does not indicate the relative degradation in power to a fresh pack.
A current method for estimating available power uses a look-up table. The table uses information about temperature, state of charge (SOC), and battery age to predict and estimate available power. This predictive method fails to account for varying degrees of degradations in battery chemistry that occur over long periods that result from varying operating environments. For example, a user operating an electric vehicle in a hot climate may experience shorter battery life due to high temperature usage.
What is needed is an apparatus and method to measure battery degradation in contrast to conventional techniques of predicting the battery degradation.